Continuous time-varying biasing approach for spectrally tunable infrared detectors

In a recently demonstrated algorithmic spectral-tuning technique by Jang et al. [Opt. Express 19, 19454-19472, (2011)], the reconstruction of an object’s emissivity at an arbitrarily specified spectral window of interest in the long-wave infrared region was achieved. The technique relied upon forming a weighted superposition of a series of photocurrents from a quantum dots-in-a-well (DWELL) photodetector operated at discrete static biases that were applied serially. Here, the technique is generalized such that a continuously varying biasing voltage is employed over an extended acquisition time, in place using a series of fixed biases over each sub-acquisition time, which totally eliminates the need for the post-processing step comprising the weighted superposition of the discrete photocurrents. To enable this capability, an algorithm is developed for designing the time-varying bias for an arbitrary spectral-sensing window of interest. Since continuous-time biasing can be implemented within the readout circuit of a focal-plane array, this generalization would pave the way for the implementation of the algorithmic spectral tuning in focal-plane arrays within in each frame time without the need for on-sensor multiplications and additions. The technique is validated by means of simulations in the context of spectrometry and object classification while using experimental data for the DWELL under realistic signal-to-noise ratios

Development of a safety education program using simulator fire extinguishers in Korea: Focusing on elementary school students

Safety education aims to promote safe habits through experience-oriented education that combines knowledge, skills  and attitudes. However, in situations where experience-oriented safety education is challenging, realistic content created through technological advancements can indirectly function as an excellent safety education tool that allows for individual safety experiences. This study conducted a safety education program for 34 elementary school students using the most commonly used realistic safety education content in Korea, the 'simulator fire extinguisher,' four times. Safety knowledge tests and safety problem-solving ability tests were used as measuring tools and statistical significance was verified through paired sample t-tests. This study demonstrated that the safety education program using the 'simulator fire extinguisher' was effective in improving safety knowledge and problem-solving abilities . The average score of elementary school students increased from 8.47 to 9.23 in safety knowledge tests and from 4.26 to 4.64 in safety problem-solving ability tests. These results were statistically significant (p < 0.001)

FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.Comment: Accepted to ICPP 2022. Code: https://github.com/hukla/fedclassav

Printing three-dimensional tissue analogues with decellularized extracellular matrix bioink

The ability to print and pattern all the components that make up a tissue (cells and matrix materials) in three dimensions to generate structures similar to tissues is an exciting prospect of bioprinting. However, the majority of the matrix materials used so far for bioprinting cannot represent the complexity of natural extracellular matrix (ECM) and thus are unable to reconstitute the intrinsic cellular morphologies and functions. Here, we develop a method for the bioprinting of cell-laden constructs with novel decellularized extracellular matrix (dECM) bioink capable of providing an optimized microenvironment conducive to the growth of three-dimensional structured tissue. We show the versatility and flexibility of the developed bioprinting process using tissue-specific dECM bioinks, including adipose, cartilage and heart tissues, capable of providing crucial cues for cells engraftment, survival and long-term function. We achieve high cell viability and functionality of the printed dECM structures using our bioprinting method.open11349353sciescopu

The Use of Pluripotent Stem Cell for Personalized Cell Therapies against Neurological Disorders

Although there are a number of weaknesses for clinical use, pluripotent stem cells are valuable sources for patient-specific cell therapies against various diseases. Backed-up by a huge number of basic researches, neuronal differentiation mechanism is well established and pluripotent stem cell therapies against neurological disorders are getting closer to clinical application. However, there are increasing needs for standardization of the sourcing pluripotent stem cells by establishing stem cell registries and banking. Global harmonization will accelerate practical use of personalized therapies using pluripotent stem cells